We are in an era of ‘Big Data’ in industrial geoscience. In commercial settings, massive data volumes are routinely acquired that challenge our ability to store and intelligently process them. The volume of data routinely acquired in industrial settings is becoming so large that it can no longer be processed in toto with traditional processing tools. In exploration seismology, for instance, it is not unusual to collect hundreds of millions of seismic traces of several thousand time samples each, which results in data volumes on the order of a trillion values. Our ability to store and mine this deluge of information is a major technical challenge. Model-oriented design is useful to the geosciences. Maurer and Curtis give a comprehensive review of the state of the art, covering topics ranging from the optimization of earthquake-monitoring networks and electromagnetic surveys, to the optimum design of seismic tomography experiments and seismic amplitude variation with offset/angle surveys. Maurer and Curtis also discuss the algorithms typically used for the optimization aspect of design, including global and sequential strategies